Artful Encounters: Sites of Visual Inquiry
Bibliographic record
Abstract
Art collections are profound research environments. They steward the creative impulses of past and contemporary societies; provide sites for critical reflection on objects and their multifarious contexts; motivate productive dialogue between objects and beholders; and engage with and reflect upon the values that drive the creation, exchange, and interpretation of the manifestations of human imagination. In the summer of 2019, a workshop hosted by the University of Guelph’s Bachinski/Chu Print Study Collection in the School of Fine Art and Music brought together a multidisciplinary group to examine the contributions of collaborative, interdisciplinary programming that use works of art for purposes beyond the immediate study of art and culture. Art historians, artists, museum professionals, and their co-investigators from a variety of STEM disciplines, along with students and community partners, convened for this two-day event. This book provides the Nexus for Innovation workshop participants a venue through which to further explore and consider more deeply the roles art can play in stimulating multi- and interdisciplinary discourse and developing intellectual and observational competencies. The authors represented in this collection of essays shed light on the constitutive nature of art as both a product and agent of creative invention. The artful encounters examined here demonstrate how the spaces created to confront works of art become important sites of productive visual inquiry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.029 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".